局部频率测量数据驱动的电力系统功率缺额在线评估方法

doi:10.16183/j.cnki.jsjtu.2021.401

摘要/Abstract

摘要：

随着新能源并网比例的不断提高,新型电力系统的惯量及频率支撑能力不断降低,导致系统在遭受扰动时易出现频率崩溃,因此需要对扰动后系统功率缺额进行快速准确评估以用于功率缺额的快速填补.提出了一种局部频率测量数据驱动的基于深度卷积和长短期记忆复合神经网络的系统功率缺额在线评估方法.首先,由于同步测量获取实际惯量中心 (COI) 频率无法适应在线评估的快速性,所以利用局部测量频率估算得到COI频率,避免了复杂通信造成的延时效应;然后设计了一种深度复合神经网络,挖掘海量频率数据和功率缺额间的关联信息;最后搭建39节点系统进行仿真验证,结果显示了所提方法的有效性和快速性.

关键词: 新型电力系统, 频率响应, 复合神经网络, 惯量中心, 数据驱动

Abstract:

With the growing penetration of renewable generation, the inertia and frequency support ability of the renewable-dominated power system are continuously reduced, which leads to frequency collapse when the system is disturbed. Therefore, it is of great significance to promptly and accurately evaluate the power shortage after a major disturbance to fill the power shortage quickly. This paper proposes an online estimation approach of power shortage in power system based on deep convolution and long-short term memory composite neural network driven by local frequency measurement data. First, since using the synchronous measurements to obtain the frequency of center of inertia (COI) cannot adapt to the rapidity of online estimation, this paper employs the local frequency measurements to estimate the COI frequency, avoiding the delay effect caused by the complex communication. Then, it designs a deep composite neural network to mine the correlation information between massive frequency data and power shortage. Finally, it tests the simulations on a 39-bus system to verify the effectiveness of the proposed approach. The results demonstrate that the proposed approach is effective and fast.

Key words: renewable-dominated power system, frequency response, composite neural network, center of inertia (COI), data-driven

中图分类号:

TM712

唐震, 郝丽花, 冯静. 局部频率测量数据驱动的电力系统功率缺额在线评估方法[J]. 上海交通大学学报, 2023, 57(4): 403-411.

TANG Zhen, HAO Lihua, FENG Jing. Online Estimation of Power Shortage in Power Systems Driven by Local Frequency Measurement Data[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 403-411.

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序号	网络层	卷积核		数量	输出大小	是否全0 填充
序号	网络层	大小	步长	数量	输出大小	是否全0 填充
1	输入层	—	—	—	50×1	否
2	卷积层1	5×1	1	64	50×64	是
3	卷积层2	5×1	1	128	50×128	是
4	池化层1	2×1	1	—	25×128	否
5	卷积层3	5×1	1	256	25×256	是
6	卷积层4	5×1	1	512	25×512	是
7	池化层2	2×1	1	—	12×256	否
8	LSTM1	—	—	80	80×1	否
9	LSTM2	—	—	60	60×1	否
10	输出层	—	—	—	1×1	否

方法	MAPE	NRE	MRE	E<5% 的概率/%	模型训练时间/s	一组数据测试时间/ ms
方法1	21.35	0.76	63.81	14.5	—	—
DCNN	3.11	0	13.91	81.0	1 914.8	0.77
BP	7.10	0	29.27	49.0	1 699.7	0.97
LSTN	3.87	0	14.80	72.5	1 988.0	1.02
DCNN- LSTM	2.74	0	11.40	87.5	2 014.8	1.32